AbstractDistributed coding of correlated grayscale stereo images is effectively addressed by a recently proposed codec that learns block-wise disparity at the decoder. Based on the Slepian-Wolf theorem, one image can be transmitted at a rate approaching the conditional entropy if the other image is referenced as side information at the decoder. This paper improves the methods in the decoder design by refining disparity estimates to pixel resolution, generating more accurate initial disparity estimates, and modeling noise as a nonstationary random field. The new decoder enables up to an additional 9 percent bit rate savings for lossless coding. When the rate is insufficient for lossless reconstruction, the new decoder improves PSNR and significantly reduces visually unpleasant blocking artifacts.

BioDavid Chen is an M.S./Ph.D. student in Electrical Engineering at Stanford University. His focus is on image processing and image systems, and his research is part of the Image, Video, and Multimedia Systems (IVMS) group. Previously, he has interned at 3DGeo, a seismic imaging company, developing a multiresolution data viewer. As an undergraduate, he worked successfully on multiple research projects for the Stanford Linear Accelerator Center (SLAC), the Space, Telecommunications, and Radioscience (STAR) Lab, and the Stanford Exploration Project (SEP).